Skip to main content

Table 1 Construction process of the uncertain C4.5 decision tree

From: RETRACTED ARTICLE: P2P net loan default risk based on Spark and complex network analysis based on wireless network element data environment

Input: the indefinite set of data D, all the attributes list attribute_list contained in D

Output: uncertain decision tree

Start:

1) create a node N;

2) If indeterminate dataset D all the tuple class labels are C;

3) return to N as a leaf node and mark as a class C;

4) Else if (attribute_list empty) then

5) return to the N node and mark with the majority of the class marks in the remaining tuples;

6) End if;

7) the information gain rate of each attribute is calculated, and the highest information gain rate is selected as the N point.

8) If (attribute is continuous or uncertain) then

9) select a split position Y;

10) For (R per unit of tuple) do

11) If (attribute = y) then

12) the weight of lD is w Rj.

13) Else if (attribute>y) then

14) the weight of rD is w Rj.

15) Else

16) to take the weight of lD from yxjdxxfw R

17) to take the weight of rD from (.Xyjdxxfw R 2)

18) End if;

19) End for;

20) Else For

21) each discrete attribute value NIA),..., 3,2,1 (I from do)

22) a direct downward division of iD branches;

23) End for;

24) End if;

25) For (each iD) do

26) according to the division rules of the decision tree, the nodes continue to be divided.

27) delete the attributes that have been partitioned from attribute_list after each partition.

28) End for;

29) End